Group 01 project: Analysis of Gene Expression in Parkinsson Disease

Rune Daucke, runda
David Faurdal, s144523
Luisa Weisch,

Introduction

Gene expression data (Affymetrix platform).

Collected from the blood of: - 20 healthy patients - 40 patients diagnosed with sporadic Parkinsons disease

Methods

Cleaning and Filtering Dataset

  • Arranging samples by condition

  • Filter out AFFX related spikes

  • Reshaping into tidy format

  • Mapping probe IDs to genes using Affymetrix database

  • Sorting samples

tidy_format

Augmentation

  • Mean expression of sets of potential biomarkers
    • Neurodegenerative-associated genes
    • Pro-Inflammation genes
# inflammation index
inflam_list <- c("IL1rn", "TNF", "Saa3", "Emr1", "Adam8", "Itgam")

present_genes <- find_genes(inflam_list, all_genes)
df_wide$inflam_mean <- rowMeans(df_wide[present_genes], na.rm = TRUE)
    • PCA
    • Differential Expression
    • Differential Expression of known biomarker gene

Results - PCA

PCA

Results - Diff. Expression

volcano

Results - Diff. Expression of known biomarkers

Neurodegenerative-associated genes

neurodegenerative_density

neurodegenerative_boxplot

Results - Diff. Expression of known biomarkers

Pro-inflammatory genes

inflammatory_density

inflammatory_boxplot

Kegg enrichment

kegg_enrichment

Kegg pathway

kegg_pathway

Conclusion

  • No clustering / Separtion along the PC’s
  • No significant differentially expressed genes
  • What to do next?
    • With patient metadata, clustering might present itself
    • Non-linear tendencies. Performing UMAP or t-SNE could provide clustering
    • ANN, KNN logistic modelling could reveal expression patterns
    • Redo the experiment
    • Perhaps blood transcriptomics on PD patient, isn’t the way to go, though biomarker discovery would be ideal.